The short answer
- An AI workflow runs steps you defined in advance. The same input takes the same path every time.
- An AI agent decides its own steps. It picks tools, reads the results, and keeps going until the goal is met.
- An agentic workflow is a workflow with agents inside it: a fixed path you control, with agents handling only the steps that need judgment.
- Rule of thumb: if you can write the steps down, build a workflow. Add an agent only where the right next step depends on the input.
Contents
"Agent" has become the word for anything with an LLM in it. That makes it hard to tell what you actually need. Is it a chatbot, a workflow with an AI step, or a fully autonomous agent? The answer changes your cost, your latency and how often you get paged at 2 a.m.
This guide explains the three ideas in plain terms, compares them side by side, and gives you a short checklist for choosing. We build workflow software for a living, and the most common mistake we see is teams reaching for an agent when a workflow would have been cheaper, faster and easier to trust.
What are AI agents?
An AI agent is software that uses a large language model (LLM) to work towards a goal on its own. You give it a task and a set of tools. It decides which tool to call, looks at the result, and chooses the next step. It stops when the task is done or it runs out of options.
Every agent has the same four building blocks:
- The LLM is the reasoning engine. It reads the situation and decides what to do next.
- Instructions (the prompt template) define the agent's role, its limits and what "done" looks like.
- Tools let the agent act on the world: call an API, query a database, search a knowledge base or send a message.
- Memory keeps what the agent has learned: intermediate results within a task, and user preferences or facts across sessions.
These pieces run in a loop. The agent plans a step, acts by calling a tool, observes the result, and repeats. That loop is what separates an agent from a single LLM call: the model, not your code, decides how many turns it takes.
AI agent vs chatbot
A chatbot answers. An agent acts. A chatbot tells a customer how to request a refund; an agent checks the order, confirms it is eligible, issues the refund and updates the ticket. Many modern "chatbots" are really agents with a chat window on the front.
What is agentic AI?
Agentic AI is the broader approach of building systems that pursue goals with little human input. An AI agent is one component. Agentic AI is the whole system: often several agents that split up a task, hand work to each other, and adapt when something unexpected happens.
Put simply, an AI agent is a worker and agentic AI is the way the work is organised. When agents cooperate, one usually acts as a supervisor that plans and delegates while the others specialise. We cover that pattern in detail in multi-agent orchestration explained.
What are AI workflows?
An AI workflow connects LLM calls and tools along a path you define in code or on a canvas. The model does useful work at each step, such as summarising, classifying or extracting, but it does not choose what happens next. Your workflow does.
Most AI workflows use a few simple patterns:
- Chaining: the output of one step feeds the next (extract, then summarise, then translate).
- Routing: a classifier sends each input down the right branch (billing, bug, sales).
- Parallel steps: several checks run at once and their results are combined.
Because the path is fixed, workflows are predictable, cheap to run and easy to debug: when something breaks, you know which step broke. For a full primer, see what is AI workflow automation.
What is an agentic workflow?
An agentic workflow is a workflow with one or more agents placed inside it. The overall path stays fixed: trigger, look-ups, approvals, final actions. Agents take over only the steps where a fixed rule would fall short, such as reading a messy email and deciding what the sender actually wants.
This is where most production systems end up. You keep the control and audit trail of a workflow, and you get an agent's flexibility exactly where you need it. Guardrails come naturally: the agent can only use the tools that step gives it, and risky actions still pass through a fixed approval step.
Agentic workflows vs AI agents: side by side
Here is how the three approaches compare on the things that matter in production.
| AI workflow | Agentic workflow | AI agent | |
|---|---|---|---|
| Who decides the next step | Your code | Your code, with agents deciding inside some steps | The LLM |
| Predictability | High: same path every run | High overall, variable inside agent steps | Low: path changes per run |
| Cost per run | Low (1–3 LLM calls) | Medium | High (many LLM calls) |
| Latency | Seconds | Seconds to a minute | Seconds to minutes |
| Debugging | Easy: find the failed step | Moderate | Hard: read the full trace |
| Handles new situations | Poorly | Well, within each agent step | Well |
| Best for | Known, repeatable processes | Business processes with a few judgment calls | Open-ended tasks such as research or coding |
When to use an AI agent: a 4-question checklist
Start with the simplest option and add complexity only when it earns its keep. Often a single well-written LLM call, with the right documents in its prompt, is enough. If it is not, ask these four questions about the task:
- Does the right path depend on the input? If you can write the steps down in advance, the answer is no, and a workflow will do. An agent helps only when the next step depends on what it finds along the way.
- Is a wrong step cheap to catch and undo? Agents will sometimes pick the wrong tool. If a mistake means a bad draft, fine. If it means money leaving the account, put that action behind a fixed approval step.
- Can success be checked automatically? Agents work best when there is a clear finish line: the tests pass, the ticket is resolved, the record matches. Without one, you cannot tell whether the agent is doing well.
- Can the user wait, and does the value cover the cost? Agents trade speed and money for better results. That trade is worth it for a support case that would otherwise need a person. It is not worth it for tagging a thousand rows.
Four yeses: an agent is a good fit. Mixed answers: build an agentic workflow and give the agent only the steps that scored "yes". Mostly no: a plain workflow will be cheaper, faster and easier to trust.
Try it on a real process
Describe a process in plain English and Flowgraph lays it out on a visual canvas, with fixed steps and agent steps clearly marked. Join the waitlist.
5 agentic workflow examples
Each example below follows the same shape: a fixed trigger and fixed actions, with an agent in the middle doing the part that needs judgment.
1. Customer support triage
Support is the best-known agentic workflow for good reason. Conversations vary too much for fixed rules, but the actions are limited and easy to check. The agent pulls customer data, order history and help articles as it needs them, and a "resolved" ticket gives a clear measure of success. Some vendors are confident enough in this that they charge only for tickets their agent actually resolves.
2. Lead qualification
The research step is open-ended, because every company's website is different, so it suits an agent. Routing and CRM updates follow your sales rules, so they stay fixed.
3. Invoice processing
Vendors send invoices in hundreds of formats. The agent handles the variety, while the payment rules stay deterministic and auditable.
4. Incident triage
The agent does the digging a tired engineer would do at 2 a.m. It never restarts or rolls back anything on its own. It suggests, and a person decides.
5. Content moderation
Cheap rules handle most content. The more expensive agent sees only the few cases the rules cannot decide, which keeps cost down.
Common mistakes to avoid
- Using an agent for a fixed process. If the steps never change, an agent only adds cost, latency and randomness.
- Giving the agent every tool. Each extra tool is another way to be wrong. Give each agent step only the tools it needs.
- No human in the loop for risky actions. Refunds, deletions and outbound emails should pass through a fixed approval step until you trust the agent's track record.
- No evaluations. Save real inputs and expected outcomes, and rerun them whenever you change a prompt or model. Without evals, every change is a guess.
- No step or budget limit. Cap the number of turns and the spend per run so a confused agent cannot loop forever.
Frequently asked questions
What is the difference between AI agents and agentic workflows?
An AI agent is an LLM-driven system that decides its own next step: which tools to call, in what order, and when to stop. An agentic workflow is a process whose overall path is fixed in code, with one or more agents handling only the steps that need judgment. The workflow gives you control; the agents inside it give you flexibility.
Is agentic AI the same as an AI agent?
No. An AI agent is a single system. Agentic AI describes the broader approach of giving AI systems goals, tools and autonomy, often with several agents working together over many steps. Every agentic AI system uses agents, but one agent on its own is only a small part of agentic AI.
Do I need an AI agent or a workflow?
Start with a workflow if you can write the steps down in advance. Use an agent only when the path depends on the input, a wrong step is cheap to catch, success can be checked automatically, and users can wait a few extra seconds. If only one step needs judgment, put an agent in that step and keep the rest as a workflow.
Are AI agents deterministic?
No. The same input can lead an agent to call different tools in a different order, because an LLM chooses each step. Narrow instructions, a small tool set, structured outputs, step limits and evaluations make agents more predictable, but a workflow is still the right choice when every run must behave identically.
Are AI agents more expensive than workflows?
Usually, yes. An agent makes several LLM calls per task as it plans, calls tools and checks its own work, so it costs more and takes longer than a workflow that makes one or two calls. The extra cost pays off when the agent resolves cases that a fixed workflow would have handed to a person.
What is an example of an agentic workflow?
Customer support triage is a common example. A new ticket triggers the workflow, fixed steps look up the customer and their orders, and an agent reads the ticket, decides whether to answer from the knowledge base, file a bug or escalate, and drafts the reply. Refunds above a set amount still go to a person for approval.
How is an AI agent different from a chatbot?
A chatbot answers messages. An AI agent takes actions: it calls APIs, queries databases, updates records and carries a task through several steps until it is done. Many support chatbots are now agents behind the scenes, because they look up orders and issue refunds instead of only replying with text.
The bottom line
Agents are powerful, but they are not the default. Build a workflow when you know the steps, add an agent where the path depends on the input, and keep risky actions behind fixed approvals. Most teams find that an agentic workflow, rather than a fully autonomous agent, gives them the best balance of flexibility and control.
Build this in Flowgraph
Describe a workflow like this in plain English and Flowgraph builds it on a visual canvas. Join the waitlist.
Further reading: Building effective agents (Anthropic) · AI Agents vs. Agentic AI: A Conceptual Taxonomy (arXiv).
Flowgraph Founders
We build Flowgraph, a visual canvas for AI workflows and agents. We write about what we learn shipping agentic systems to production.